Jennifer Lyn Morone, an American artist, thinks this is the state in which most people now live. To get free online services, she laments, they hand over intimate information to technology firms. "Personal data are much more valuable than you think," she says. To highlight this sorry state of affairs, Ms Morone has resorted to what she calls "extreme capitalism": she registered herself as a company in Delaware in an effort to exploit her personal data for financial gain. She created dossiers containing different subsets of data, which she displayed in a London gallery in 2016 and offered for sale, starting at £100 ($135). The entire collection, including her health data and social-security number, can be had for £7,000.

University of Toronto researchers have designed an algorithm to disrupt facial recognition technology. The past few months have witnessed a mainstream groundswell around security and data privacy, embodied most notably in reaction to news of Cambridge Analytica's data-collection tactics and Facebook CEO Mark Zuckerberg's testimony before the U.S. Senate. One major form of data emerges from facial recognition technology, which uses algorithms to identify us based on facial feature points. Every time you upload a photo to Facebook, Instagram, or otherwise, you give these learning systems another data point around your face -- and anybody else in the picture with you -- as well as metadata such as phone type and location. To address this problem, researchers at the University of Toronto, led by Professor Parham Aarabi and graduate student Avishek Bose, have developed an algorithm to dynamically disrupt this technology.

In the previous article, we talked about the nature of human fears in relation to artificial intelligence (AI). We also outlined two important themes to address in the upcoming challenges of the AI era: the societal, and decision-making aspects of AI systems. In our new article I expand on the theme of the impact on society, and I share our opinion on possible steps we should take to anticipate and adjust to it. McKinsey analysts estimate the automation potential for all economic sectors to be around 50%. This means that around half of all the activities people in the world's workforce are paid to do today could potentially be automated with currently available technologies.

Understanding how a robot will react under different conditions is essential to guaranteeing its safe operation. But how do you know what will break a robot without actually damaging it? A new method developed by scientists at the Institute of Science and Technology Austria (IST Austria) and the Max Planck Institute for Intelligent Systems (MPI for Intelligent Systems) is the first machine learning method that can use observations made under safe conditions to make accurate predictions for all possible conditions governed by the same physical dynamics. Especially designed for real-life situations, their method provides simple, interpretable descriptions of the underlying physics. The researchers will present their findings tomorrow at this year's prestigious International Conference for Machine Learning (ICML). In the past, machine learning was only capable of interpolating data--making predictions about situations that are "between" other, known situations.

Cameras and GPS navigation system gear are placed on a self-driving Mercedes car on display at an event to present a project on autonomous driving at former Tempelhof airport on July 10, 2018 in Berlin, Germany. Discussion about autonomous driving tends to focus on the leading company in the field, Google, which back in October 2010 set the ball rolling. Following the restructure that led to the creation of Alphabet, a subsidiary was set up called Waymo, which already has fleets of self-driving cars without safety driver on the roads of Phoenix, Mountain View, San Francisco, Austin, Detroit, Atlanta and Kirkland, selected mainly for their geographic and meteorological conditions. Which is not to say other companies aren't actively pursuing their own autonomous vehicles. In Arizona and several other cities, Cruise, owned by GM, has a large fleet.

Can't code, or speak Bahasa? Didn't go to school with a CEO's son or daughter? A robot will take your trading seat. Read on if you want to save your job. The threat from automation is in the flows part of banks' global markets business, the most important chunk of the biggest division of investment banking.

PARIS – At the heart of the spread of fake news are the algorithms used by search engines, websites and social media, which are often accused of pushing false or manipulated information regardless of the consequences. They are the invisible but essential computer programs and formulas that increasingly run modern life, designed to repeatedly solve recurrent problems or to make decisions on their own. Their ability to filter and seek out links in gigantic databases means it would be impossible to run global markets without them, but they can also be refined down to produce personalized quotes on everything from mortgages to plane tickets. They also run our Google searches, our Facebook news feed, recommend articles or videos to us and sometimes censor questionable content because it may contain violence, pornography or racist language. Other algorithms charged with the most complex and sensitive tasks can be opaque "black boxes" that develop their own artificial intelligence based on our data.

Digital transformation of the finance function can improve transactional performance and deliver insights that create and sustain value across the entire enterprise. Think of CFOs, and who do you picture? Typically, you might see them as prudent people. They're not the category of board members you might traditionally regard as the first adopters of technology. But times have changed, and it's time to look again.

Traditional information retrieval systems represent documents and queries by keyword sets. However, the content of a document or a query is mainly defined by both keywords and named entities occurring in it. Named entities have ontological features, namely, their aliases, classes, and identifiers, which are hidden from their textual appearance. Besides, the meaning of a query may imply latent named entities that are related to the apparent ones in the query. We propose an ontology-based generalized vector space model to semantic text search. It exploits ontological features of named entities and their latently related ones to reveal the semantics of documents and queries. We also propose a framework to combine different ontologies to take their complementary advantages for semantic annotation and searching.

This paper introduces a new sparse spatio-temporal structured Gaussian process regression framework for online and offline Bayesian inference. This is the first framework that gives a time-evolving representation of the interdependencies between the components of the sparse signal of interest. A hierarchical Gaussian process describes such structure and the interdependencies are represented via the covariance matrices of the prior distributions. The inference is based on the expectation propagation method and the theoretical derivation of the posterior distribution is provided in the paper. The inference framework is thoroughly evaluated over synthetic, real video and electroencephalography (EEG) data where the spatio-temporal evolving patterns need to be reconstructed with high accuracy. It is shown that it achieves 15% improvement of the F-measure compared with the alternating direction method of multipliers, spatio-temporal sparse Bayesian learning method and one-level Gaussian process model. Additionally, the required memory for the proposed algorithm is less than in the one-level Gaussian process model. This structured sparse regression framework is of broad applicability to source localisation and object detection problems with sparse signals.